Commuting to School in Leeds : How useful is the PLASC?

Children's daily travel behaviour is dominated by the journey to school. In some cases, this movement takes only a few minutes and involves no means of transport other than foot; in other instances, the journey can be over substantial distances, be extensive in duration and involve some form of public or private transport. The combination of journeys taking place is likely to have a substantial impact on traffic congestion, particularly since the morning peak coincides with that associated with the journey to work. What datasets exist that allow us to measure and understand this behaviour

No such thing as a free lunch? Exploring the consistency, validity, and uses of the 'Free School Meals' (FSM) measure in the National Pupil Database

This working paper discusses and synthesises existing literature on correspondences between attribution of Free School Meals (FSM) status in the National Pupil Database (NPD) and family income-level and other circumstances. It then presents new descriptive comparisons of FSM status as recorded in the Reception year of primary school, and other recorded child, national, and area-level factors. Compounding known issues with patterns of under-recording in the NPD of FSM-entitled children, changing criteria for entitlement to FSM, and fuzziness in definition and usage – in terms of what recorded FSM status is assumed to represent – findings here indicate that the compositions of the FSM vs non-FSM-ascribed groups have changed over time. They suggest moreover that the relationships between area-level factors and FSM attribution may also have altered over the past decade, and point to increased under-ascription of children from low-income families / families living in poverty. The potential consequences of these findings for mapping of ‘gaps’ in time series comparing FSM and non-FSM-attributed children and for evaluation (and perhaps formulation) of targeted policies and interventions are discussed. Implications of differential attributions of FSM status within a system where ascription is not straightforward nor entirely ‘accurate’ but where it is deterministic of experiences and outcomes begin to be explored, laying the foundations for further research

Recognition of gait patterns in human motor disorders using a machine learning approach

Dissertação de mestrado em Industrial Electronics and Computers EngineeringWith advanced age, the occurrence of motor disturbances becomes more prevalent and can lead to gait pathologies, increasing the risk of falls. Currently, there are many available gait monitoring systems that can aid in gait disorder diagnosis by extracting relevant data from a subject’s gait. This increases the amount of data to be processed in working time. To accelerate this process and provide an objective tool for a systematic clinical diagnosis support, Machine Learning methods are a powerful addition capable of processing great amounts of data and uncover non-linear relationships in data. The purpose of this dissertation is the development of a gait pattern recognition system based on a Machine Learning approach for the support of clinical diagnosis of post-stroke gait. This includes the development of a data estimation tool capable of computing several features from inertial sensors. Four different neural networks were be added to the classification tool: Feed-Forward (FFNN), convolutional (CNN) and two recurrent neural networks (LSTM and CLSTM). The performance of all classification models was analyzed and compared in order to select the most effective method of gait analysis. The performance metric used is Matthew’s Correlation Coefficient. The classifiers that exhibit the best performance where Support Vector Machines (SVM), k-Nearest Neighbors (KNN), CNN, LSTM and CLSTM, with a Matthew’s correlation coeficient of 1 in the test set. Despite the first two classifiers reaching the same performance of the three neural networks, the later reached this performance systematically and without the need of explicit dimensionality reduction methods.Com o avançar da idade, a ocorrência de distúrbios motores torna-se mais prevalente, conduzindo a patologias na marcha e aumentando o risco de quedas. Atualmente, muitos sistemas de monitorização de marcha extraem grandes quantidades de dados biomecânicos para apoio ao diagnóstico clínico, aumentando a quantidade de dados a ser processados em tempo útil. Para acelerar esse processo e proporcionar uma ferramenta objetiva de apoio sistemático ao diagnóstico clínico, métodos de Machine Learning são uma poderosa adição, processando grandes quantidades de dados e descobrindo relações não-lineares entre dados. Esta dissertação tem o objetivo de desenvolver um sistema de reconhecimento de padrões de marcha com uma abordagem de Machine Learning para apoio ao diagnóstico clínico da marcha de vitimas de AVC. Isso inclui o desenvolvimento de uma ferramenta de estimação de dados biomecânicos e cálculo de features, a partir de sensores inerciais. Quatro redes neuronais foram implementadas numa ferramenta de classificação: uma rede Feed-Forward (FFNN), uma convolucinal (CNN), e duas redes recorrentes (LSTM e CLSTM). O desempenho de todos os modelos de classificação foi analisado. A métrica de desempenho usada é o coeficiente de correlação de Matthew. Os classificadores com melhor performance foram: Support Vector Machines (SVM), k-Nearest Neighbors (KNN), CNN, LSTM e CLSTM. Todos com uma performance igual a 1 no conjunto de teste. Apesar de os dois primeiros classificadores atingirem a mesma performance das redes neuronais, estas atingiram esta performance repetidamente e sem necessitar de métodos de redução de dimensionalidade

An analysis of fire safety legislations and fire safety management for hotels in Hong Kong

Includes bibliographical references (p. 188-201).Thesis (B.Sc)--University of Hong Kong, 2008.published_or_final_versio

A hybrid and cross-protocol architecture with semantics and syntax awareness to improve intrusion detection efficiency in Voice over IP environments

Includes abstract.Includes bibliographical references (leaves 134-140).Voice and data have been traditionally carried on different types of networks based on different technologies, namely, circuit switching and packet switching respectively. Convergence in networks enables carrying voice, video, and other data on the same packet-switched infrastructure, and provides various services related to these kinds of data in a unified way. Voice over Internet Protocol (VoIP) stands out as the standard that benefits from convergence by carrying voice calls over the packet-switched infrastructure of the Internet. Although sharing the same physical infrastructure with data networks makes convergence attractive in terms of cost and management, it also makes VoIP environments inherit all the security weaknesses of Internet Protocol (IP). In addition, VoIP networks come with their own set of security concerns. Voice traffic on converged networks is packet-switched and vulnerable to interception with the same techniques used to sniff other traffic on a Local Area Network (LAN) or Wide Area Network (WAN). Denial of Service attacks (DoS) are among the most critical threats to VoIP due to the disruption of service and loss of revenue they cause. VoIP systems are supposed to provide the same level of security provided by traditional Public Switched Telephone Networks (PSTNs), although more functionality and intelligence are distributed to the endpoints, and more protocols are involved to provide better service. A new design taking into consideration all the above factors with better techniques in Intrusion Detection are therefore needed. This thesis describes the design and implementation of a host-based Intrusion Detection System (IDS) that targets VoIP environments. Our intrusion detection system combines two types of modules for better detection capabilities, namely, a specification-based and a signaturebased module. Our specification-based module takes the specifications of VoIP applications and protocols as the detection baseline. Any deviation from the protocol’s proper behavior described by its specifications is considered anomaly. The Communicating Extended Finite State Machines model (CEFSMs) is used to trace the behavior of the protocols involved in VoIP, and to help exchange detection results among protocols in a stateful and cross-protocol manner. The signature-based module is built in part upon State Transition Analysis Techniques which are used to model and detect computer penetrations. Both detection modules allow for protocol-syntax and protocol-semantics awareness. Our intrusion detection uses the aforementioned techniques to cover the threats propagated via low-level protocols such as IP, ICMP, UDP, and TCP